Overview

Dataset statistics

Number of variables17
Number of observations205
Missing cells643
Missing cells (%)18.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.4 KiB
Average record size in memory136.6 B

Variable types

Numeric5
Text7
Categorical1
DateTime3
Unsupported1

Alerts

airdate has constant value ""Constant
id_embedded is highly overall correlated with seasonHigh correlation
season is highly overall correlated with id_embeddedHigh correlation
number is highly overall correlated with typeHigh correlation
type is highly overall correlated with numberHigh correlation
type is highly imbalanced (95.6%)Imbalance
airtime has 161 (78.5%) missing valuesMissing
runtime has 11 (5.4%) missing valuesMissing
rating_average has 205 (100.0%) missing valuesMissing
medium has 85 (41.5%) missing valuesMissing
original has 85 (41.5%) missing valuesMissing
summary has 95 (46.3%) missing valuesMissing
id has unique valuesUnique
url has unique valuesUnique
_links_self has unique valuesUnique
rating_average is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-05 19:16:49.397239
Analysis finished2023-08-05 19:16:53.048665
Duration3.65 seconds
Software versionydata-profiling vv4.4.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2446727.3
Minimum2222678
Maximum2607223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:53.136205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2222678
5-th percentile2399776.2
Q12436733
median2449801
Q32451929
95-th percentile2509498
Maximum2607223
Range384545
Interquartile range (IQR)15196

Descriptive statistics

Standard deviation36783.371
Coefficient of variation (CV)0.015033703
Kurtosis11.070734
Mean2446727.3
Median Absolute Deviation (MAD)7760
Skewness0.093185653
Sum5.0157909 × 108
Variance1.3530164 × 109
MonotonicityNot monotonic
2023-08-05T14:16:53.306003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2401587 1
 
0.5%
2449931 1
 
0.5%
2449808 1
 
0.5%
2449809 1
 
0.5%
2449810 1
 
0.5%
2449811 1
 
0.5%
2449812 1
 
0.5%
2449916 1
 
0.5%
2449917 1
 
0.5%
2449918 1
 
0.5%
Other values (195) 195
95.1%
ValueCountFrequency (%)
2222678 1
0.5%
2330730 1
0.5%
2374217 1
0.5%
2376552 1
0.5%
2380884 1
0.5%
2383899 1
0.5%
2393061 1
0.5%
2393402 1
0.5%
2393546 1
0.5%
2399775 1
0.5%
ValueCountFrequency (%)
2607223 1
0.5%
2607222 1
0.5%
2582369 1
0.5%
2574164 1
0.5%
2540850 1
0.5%
2536875 1
0.5%
2527881 1
0.5%
2524703 1
0.5%
2521740 1
0.5%
2517175 1
0.5%

id_embedded
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58020.937
Minimum4175
Maximum70186
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:53.468570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4175
5-th percentile35820
Q156994
median63881
Q365796
95-th percentile66417
Maximum70186
Range66011
Interquartile range (IQR)8802

Descriptive statistics

Standard deviation13153.139
Coefficient of variation (CV)0.22669643
Kurtosis4.8905505
Mean58020.937
Median Absolute Deviation (MAD)2281
Skewness-2.2174582
Sum11894292
Variance1.7300507 × 108
MonotonicityNot monotonic
2023-08-05T14:16:53.625076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35820 10
 
4.9%
60522 8
 
3.9%
63613 8
 
3.9%
65800 8
 
3.9%
59275 7
 
3.4%
66000 7
 
3.4%
65796 6
 
2.9%
65429 6
 
2.9%
66417 6
 
2.9%
65373 6
 
2.9%
Other values (106) 133
64.9%
ValueCountFrequency (%)
4175 1
 
0.5%
7847 1
 
0.5%
8035 1
 
0.5%
8752 2
 
1.0%
12060 1
 
0.5%
19111 1
 
0.5%
19499 1
 
0.5%
26350 1
 
0.5%
29511 1
 
0.5%
35820 10
4.9%
ValueCountFrequency (%)
70186 2
 
1.0%
69389 1
 
0.5%
68575 1
 
0.5%
68441 1
 
0.5%
67998 1
 
0.5%
67157 1
 
0.5%
66849 1
 
0.5%
66819 1
 
0.5%
66417 6
2.9%
66233 2
 
1.0%

url
Text

UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:54.028174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length147
Median length100
Mean length77.443902
Min length57

Characters and Unicode

Total characters15876
Distinct characters40
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)100.0%

Sample

1st rowhttps://www.tvmaze.com/episodes/2401587/po-sezonu-videodajdzest-seasonvar-8x50-vypusk-408
2nd rowhttps://www.tvmaze.com/episodes/2449955/kontakty-1x79-kontakty-v-telefone-vaceslava-cepurcenko-ila-averbuh-anatolij-coj-denis-kosakov-vala-karnaval
3rd rowhttps://www.tvmaze.com/episodes/2454680/krutiksy-2x04-podvodnyj-novyj-god
4th rowhttps://www.tvmaze.com/episodes/2422258/trudnye-podrostki-realnost-2x18-seria-18
5th rowhttps://www.tvmaze.com/episodes/2521740/engineering-reborn-1x04-university-of-chicago-hk-beloit-college-convent-de-sant-francesc
ValueCountFrequency (%)
https://www.tvmaze.com/episodes/2401587/po-sezonu-videodajdzest-seasonvar-8x50-vypusk-408 1
 
0.5%
https://www.tvmaze.com/episodes/2418533/qi-refining-for-3000-years-1x12-episode-12 1
 
0.5%
https://www.tvmaze.com/episodes/2454680/krutiksy-2x04-podvodnyj-novyj-god 1
 
0.5%
https://www.tvmaze.com/episodes/2422258/trudnye-podrostki-realnost-2x18-seria-18 1
 
0.5%
https://www.tvmaze.com/episodes/2521740/engineering-reborn-1x04-university-of-chicago-hk-beloit-college-convent-de-sant-francesc 1
 
0.5%
https://www.tvmaze.com/episodes/2411106/wanmei-shijie-1x89-episode-89 1
 
0.5%
https://www.tvmaze.com/episodes/2463635/supreme-god-emperor-1x231-episode-231 1
 
0.5%
https://www.tvmaze.com/episodes/2330730/against-the-sky-supreme-1x154-episode-154 1
 
0.5%
https://www.tvmaze.com/episodes/2513101/jiu-tian-xuan-di-jue-3x14-episode-106 1
 
0.5%
https://www.tvmaze.com/episodes/2427755/romance-and-bullets-1x08-episode-8 1
 
0.5%
Other values (195) 195
95.1%
2023-08-05T14:16:54.465558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1344
 
8.5%
- 1190
 
7.5%
/ 1025
 
6.5%
t 1014
 
6.4%
s 999
 
6.3%
o 887
 
5.6%
w 676
 
4.3%
a 633
 
4.0%
i 587
 
3.7%
m 581
 
3.7%
Other values (30) 6940
43.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10818
68.1%
Decimal Number 2228
 
14.0%
Other Punctuation 1640
 
10.3%
Dash Punctuation 1190
 
7.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1344
12.4%
t 1014
 
9.4%
s 999
 
9.2%
o 887
 
8.2%
w 676
 
6.2%
a 633
 
5.9%
i 587
 
5.4%
m 581
 
5.4%
p 565
 
5.2%
d 467
 
4.3%
Other values (16) 3065
28.3%
Decimal Number
ValueCountFrequency (%)
2 381
17.1%
4 351
15.8%
1 328
14.7%
0 301
13.5%
9 170
7.6%
3 166
7.5%
5 163
7.3%
8 133
 
6.0%
7 129
 
5.8%
6 106
 
4.8%
Other Punctuation
ValueCountFrequency (%)
/ 1025
62.5%
. 410
 
25.0%
: 205
 
12.5%
Dash Punctuation
ValueCountFrequency (%)
- 1190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10818
68.1%
Common 5058
31.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1344
12.4%
t 1014
 
9.4%
s 999
 
9.2%
o 887
 
8.2%
w 676
 
6.2%
a 633
 
5.9%
i 587
 
5.4%
m 581
 
5.4%
p 565
 
5.2%
d 467
 
4.3%
Other values (16) 3065
28.3%
Common
ValueCountFrequency (%)
- 1190
23.5%
/ 1025
20.3%
. 410
 
8.1%
2 381
 
7.5%
4 351
 
6.9%
1 328
 
6.5%
0 301
 
6.0%
: 205
 
4.1%
9 170
 
3.4%
3 166
 
3.3%
Other values (4) 531
10.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 1344
 
8.5%
- 1190
 
7.5%
/ 1025
 
6.5%
t 1014
 
6.4%
s 999
 
6.3%
o 887
 
5.6%
w 676
 
4.3%
a 633
 
4.0%
i 587
 
3.7%
m 581
 
3.7%
Other values (30) 6940
43.7%

name
Text

Distinct172
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:54.811331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length98
Median length50
Mean length15.980488
Min length3

Characters and Unicode

Total characters3276
Distinct characters120
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique159 ?
Unique (%)77.6%

Sample

1st rowВыпуск 408
2nd rowКОНТАКТЫ в телефоне Вячеслава Чепурченко: Илья Авербух, Анатолий Цой, Денис Косяков, Валя Карнавал
3rd rowПодводный Новый год
4th rowСерия 18
5th rowUniversity of Chicago HK, Beloit College, Convent De Sant Francesc
ValueCountFrequency (%)
episode 63
 
10.5%
the 24
 
4.0%
13
 
2.2%
a 13
 
2.2%
of 9
 
1.5%
9 7
 
1.2%
2 7
 
1.2%
1 7
 
1.2%
6 6
 
1.0%
to 6
 
1.0%
Other values (374) 444
74.1%
2023-08-05T14:16:55.338745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
394
 
12.0%
e 288
 
8.8%
o 200
 
6.1%
i 182
 
5.6%
s 160
 
4.9%
a 159
 
4.9%
r 143
 
4.4%
t 142
 
4.3%
n 117
 
3.6%
d 114
 
3.5%
Other values (110) 1377
42.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2126
64.9%
Uppercase Letter 490
 
15.0%
Space Separator 394
 
12.0%
Decimal Number 138
 
4.2%
Other Punctuation 98
 
3.0%
Dash Punctuation 19
 
0.6%
Other Letter 6
 
0.2%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 288
13.5%
o 200
 
9.4%
i 182
 
8.6%
s 160
 
7.5%
a 159
 
7.5%
r 143
 
6.7%
t 142
 
6.7%
n 117
 
5.5%
d 114
 
5.4%
l 90
 
4.2%
Other values (42) 531
25.0%
Uppercase Letter
ValueCountFrequency (%)
E 76
15.5%
T 35
 
7.1%
A 34
 
6.9%
C 29
 
5.9%
F 28
 
5.7%
D 27
 
5.5%
B 25
 
5.1%
S 24
 
4.9%
H 22
 
4.5%
M 19
 
3.9%
Other values (27) 171
34.9%
Other Punctuation
ValueCountFrequency (%)
. 52
53.1%
, 14
 
14.3%
" 12
 
12.2%
' 9
 
9.2%
! 4
 
4.1%
/ 2
 
2.0%
: 2
 
2.0%
# 1
 
1.0%
1
 
1.0%
& 1
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 31
22.5%
2 22
15.9%
9 17
12.3%
8 15
10.9%
4 11
 
8.0%
3 10
 
7.2%
5 9
 
6.5%
6 9
 
6.5%
0 7
 
5.1%
7 7
 
5.1%
Other Letter
ValueCountFrequency (%)
ل 2
33.3%
ة 1
16.7%
ق 1
16.7%
ح 1
16.7%
ا 1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 18
94.7%
1
 
5.3%
Space Separator
ValueCountFrequency (%)
394
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2506
76.5%
Common 654
 
20.0%
Cyrillic 110
 
3.4%
Arabic 6
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 288
 
11.5%
o 200
 
8.0%
i 182
 
7.3%
s 160
 
6.4%
a 159
 
6.3%
r 143
 
5.7%
t 142
 
5.7%
n 117
 
4.7%
d 114
 
4.5%
l 90
 
3.6%
Other values (43) 911
36.4%
Cyrillic
ValueCountFrequency (%)
о 10
 
9.1%
е 9
 
8.2%
а 7
 
6.4%
в 7
 
6.4%
н 6
 
5.5%
л 6
 
5.5%
я 5
 
4.5%
й 4
 
3.6%
р 4
 
3.6%
с 4
 
3.6%
Other values (26) 48
43.6%
Common
ValueCountFrequency (%)
394
60.2%
. 52
 
8.0%
1 31
 
4.7%
2 22
 
3.4%
- 18
 
2.8%
9 17
 
2.6%
8 15
 
2.3%
, 14
 
2.1%
" 12
 
1.8%
4 11
 
1.7%
Other values (16) 68
 
10.4%
Arabic
ValueCountFrequency (%)
ل 2
33.3%
ة 1
16.7%
ق 1
16.7%
ح 1
16.7%
ا 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3149
96.1%
Cyrillic 110
 
3.4%
None 8
 
0.2%
Arabic 6
 
0.2%
Punctuation 2
 
0.1%
Letterlike Symbols 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
394
 
12.5%
e 288
 
9.1%
o 200
 
6.4%
i 182
 
5.8%
s 160
 
5.1%
a 159
 
5.0%
r 143
 
4.5%
t 142
 
4.5%
n 117
 
3.7%
d 114
 
3.6%
Other values (62) 1250
39.7%
Cyrillic
ValueCountFrequency (%)
о 10
 
9.1%
е 9
 
8.2%
а 7
 
6.4%
в 7
 
6.4%
н 6
 
5.5%
л 6
 
5.5%
я 5
 
4.5%
й 4
 
3.6%
р 4
 
3.6%
с 4
 
3.6%
Other values (26) 48
43.6%
None
ValueCountFrequency (%)
å 3
37.5%
ø 3
37.5%
è 1
 
12.5%
ç 1
 
12.5%
Arabic
ValueCountFrequency (%)
ل 2
33.3%
ة 1
16.7%
ق 1
16.7%
ح 1
16.7%
ا 1
16.7%
Punctuation
ValueCountFrequency (%)
1
50.0%
1
50.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

season
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.609756
Minimum1
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:55.493118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile14
Maximum2022
Range2021
Interquartile range (IQR)1

Descriptive statistics

Standard deviation199.01811
Coefficient of variation (CV)8.8023114
Kurtosis99.802902
Mean22.609756
Median Absolute Deviation (MAD)0
Skewness10.037484
Sum4635
Variance39608.21
MonotonicityNot monotonic
2023-08-05T14:16:55.632040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 134
65.4%
2 25
 
12.2%
4 12
 
5.9%
3 11
 
5.4%
5 4
 
2.0%
18 3
 
1.5%
14 3
 
1.5%
7 2
 
1.0%
2022 2
 
1.0%
47 1
 
0.5%
Other values (8) 8
 
3.9%
ValueCountFrequency (%)
1 134
65.4%
2 25
 
12.2%
3 11
 
5.4%
4 12
 
5.9%
5 4
 
2.0%
7 2
 
1.0%
8 1
 
0.5%
9 1
 
0.5%
12 1
 
0.5%
13 1
 
0.5%
ValueCountFrequency (%)
2022 2
1.0%
47 1
 
0.5%
34 1
 
0.5%
28 1
 
0.5%
23 1
 
0.5%
22 1
 
0.5%
18 3
1.5%
14 3
1.5%
13 1
 
0.5%
12 1
 
0.5%

number
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)20.6%
Missing1
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean14.25
Minimum1
Maximum251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:55.784462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median6
Q310.25
95-th percentile53.4
Maximum251
Range250
Interquartile range (IQR)7.25

Descriptive statistics

Standard deviation31.088801
Coefficient of variation (CV)2.1816702
Kurtosis30.70449
Mean14.25
Median Absolute Deviation (MAD)3
Skewness5.1232272
Sum2907
Variance966.51355
MonotonicityNot monotonic
2023-08-05T14:16:55.940529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
1 22
10.7%
2 22
10.7%
6 20
 
9.8%
4 19
 
9.3%
5 19
 
9.3%
3 18
 
8.8%
7 12
 
5.9%
9 10
 
4.9%
8 7
 
3.4%
12 5
 
2.4%
Other values (32) 50
24.4%
ValueCountFrequency (%)
1 22
10.7%
2 22
10.7%
3 18
8.8%
4 19
9.3%
5 19
9.3%
6 20
9.8%
7 12
5.9%
8 7
 
3.4%
9 10
4.9%
10 4
 
2.0%
ValueCountFrequency (%)
251 1
0.5%
231 1
0.5%
154 1
0.5%
141 1
0.5%
112 1
0.5%
91 1
0.5%
89 2
1.0%
79 1
0.5%
70 1
0.5%
54 1
0.5%

type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
regular
204 
insignificant_special
 
1

Length

Max length21
Median length7
Mean length7.0682927
Min length7

Characters and Unicode

Total characters1449
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.5%

Sample

1st rowregular
2nd rowregular
3rd rowregular
4th rowregular
5th rowregular

Common Values

ValueCountFrequency (%)
regular 204
99.5%
insignificant_special 1
 
0.5%

Length

2023-08-05T14:16:56.088579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-05T14:16:56.205868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
regular 204
99.5%
insignificant_special 1
 
0.5%

Most occurring characters

ValueCountFrequency (%)
r 408
28.2%
a 206
14.2%
e 205
14.1%
g 205
14.1%
l 205
14.1%
u 204
14.1%
i 5
 
0.3%
n 3
 
0.2%
s 2
 
0.1%
c 2
 
0.1%
Other values (4) 4
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1448
99.9%
Connector Punctuation 1
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 408
28.2%
a 206
14.2%
e 205
14.2%
g 205
14.2%
l 205
14.2%
u 204
14.1%
i 5
 
0.3%
n 3
 
0.2%
s 2
 
0.1%
c 2
 
0.1%
Other values (3) 3
 
0.2%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1448
99.9%
Common 1
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 408
28.2%
a 206
14.2%
e 205
14.2%
g 205
14.2%
l 205
14.2%
u 204
14.1%
i 5
 
0.3%
n 3
 
0.2%
s 2
 
0.1%
c 2
 
0.1%
Other values (3) 3
 
0.2%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1449
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 408
28.2%
a 206
14.2%
e 205
14.1%
g 205
14.1%
l 205
14.1%
u 204
14.1%
i 5
 
0.3%
n 3
 
0.2%
s 2
 
0.1%
c 2
 
0.1%
Other values (4) 4
 
0.3%

airdate
Date

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2022-12-16 00:00:00
Maximum2022-12-16 00:00:00
2023-08-05T14:16:56.307909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:56.410089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

airtime
Date

MISSING 

Distinct19
Distinct (%)43.2%
Missing161
Missing (%)78.5%
Memory size1.7 KiB
Minimum2023-08-05 00:00:00
Maximum2023-08-05 22:30:00
2023-08-05T14:16:56.522663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:56.659854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
Distinct27
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum2022-12-16 00:00:00+00:00
Maximum2022-12-17 06:00:00+00:00
2023-08-05T14:16:56.811322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:56.954586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

runtime
Real number (ℝ)

MISSING 

Distinct60
Distinct (%)30.9%
Missing11
Missing (%)5.4%
Infinite0
Infinite (%)0.0%
Mean38.108247
Minimum1
Maximum180
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:57.106771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.65
Q124
median35
Q345
95-th percentile60
Maximum180
Range179
Interquartile range (IQR)21

Descriptive statistics

Standard deviation28.937912
Coefficient of variation (CV)0.75936086
Kurtosis11.373338
Mean38.108247
Median Absolute Deviation (MAD)11
Skewness2.9394265
Sum7393
Variance837.40273
MonotonicityNot monotonic
2023-08-05T14:16:57.270882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 20
 
9.8%
24 9
 
4.4%
40 7
 
3.4%
5 7
 
3.4%
30 7
 
3.4%
51 6
 
2.9%
10 6
 
2.9%
25 6
 
2.9%
36 6
 
2.9%
43 5
 
2.4%
Other values (50) 115
56.1%
(Missing) 11
 
5.4%
ValueCountFrequency (%)
1 1
 
0.5%
3 1
 
0.5%
4 1
 
0.5%
5 7
3.4%
6 2
 
1.0%
7 1
 
0.5%
8 2
 
1.0%
10 6
2.9%
12 4
2.0%
14 2
 
1.0%
ValueCountFrequency (%)
180 2
1.0%
174 1
 
0.5%
166 1
 
0.5%
165 1
 
0.5%
136 1
 
0.5%
120 2
1.0%
74 1
 
0.5%
60 3
1.5%
59 1
 
0.5%
58 2
1.0%

rating_average
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing205
Missing (%)100.0%
Memory size1.7 KiB

medium
Text

MISSING 

Distinct120
Distinct (%)100.0%
Missing85
Missing (%)41.5%
Memory size1.7 KiB
2023-08-05T14:16:57.518291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length73
Median length73
Mean length73
Min length73

Characters and Unicode

Total characters8760
Distinct characters32
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/medium_landscape/436/1090582.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/435/1088374.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/medium_landscape/435/1088460.jpg
4th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/435/1088461.jpg
5th rowhttps://static.tvmaze.com/uploads/images/medium_landscape/435/1088513.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/medium_landscape/435/1088342.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1088438.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1088374.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1088460.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1088461.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1088513.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1089133.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1089134.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1089135.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/medium_landscape/435/1089136.jpg 1
 
0.8%
Other values (110) 110
91.7%
2023-08-05T14:16:57.897181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 840
 
9.6%
a 720
 
8.2%
s 600
 
6.8%
m 600
 
6.8%
t 600
 
6.8%
p 480
 
5.5%
e 480
 
5.5%
i 360
 
4.1%
c 360
 
4.1%
. 360
 
4.1%
Other values (22) 3360
38.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6120
69.9%
Other Punctuation 1320
 
15.1%
Decimal Number 1200
 
13.7%
Connector Punctuation 120
 
1.4%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 720
11.8%
s 600
9.8%
m 600
9.8%
t 600
9.8%
p 480
 
7.8%
e 480
 
7.8%
i 360
 
5.9%
c 360
 
5.9%
d 360
 
5.9%
l 240
 
3.9%
Other values (8) 1320
21.6%
Decimal Number
ValueCountFrequency (%)
8 195
16.2%
4 188
15.7%
1 188
15.7%
3 187
15.6%
0 170
14.2%
5 114
9.5%
9 62
 
5.2%
6 45
 
3.8%
7 26
 
2.2%
2 25
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 840
63.6%
. 360
27.3%
: 120
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6120
69.9%
Common 2640
30.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 720
11.8%
s 600
9.8%
m 600
9.8%
t 600
9.8%
p 480
 
7.8%
e 480
 
7.8%
i 360
 
5.9%
c 360
 
5.9%
d 360
 
5.9%
l 240
 
3.9%
Other values (8) 1320
21.6%
Common
ValueCountFrequency (%)
/ 840
31.8%
. 360
13.6%
8 195
 
7.4%
4 188
 
7.1%
1 188
 
7.1%
3 187
 
7.1%
0 170
 
6.4%
_ 120
 
4.5%
: 120
 
4.5%
5 114
 
4.3%
Other values (4) 158
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 8760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 840
 
9.6%
a 720
 
8.2%
s 600
 
6.8%
m 600
 
6.8%
t 600
 
6.8%
p 480
 
5.5%
e 480
 
5.5%
i 360
 
4.1%
c 360
 
4.1%
. 360
 
4.1%
Other values (22) 3360
38.4%

original
Text

MISSING 

Distinct120
Distinct (%)100.0%
Missing85
Missing (%)41.5%
Memory size1.7 KiB
2023-08-05T14:16:58.152360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length75
Mean length75
Min length75

Characters and Unicode

Total characters9000
Distinct characters33
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)100.0%

Sample

1st rowhttps://static.tvmaze.com/uploads/images/original_untouched/436/1090582.jpg
2nd rowhttps://static.tvmaze.com/uploads/images/original_untouched/435/1088374.jpg
3rd rowhttps://static.tvmaze.com/uploads/images/original_untouched/435/1088460.jpg
4th rowhttps://static.tvmaze.com/uploads/images/original_untouched/435/1088461.jpg
5th rowhttps://static.tvmaze.com/uploads/images/original_untouched/435/1088513.jpg
ValueCountFrequency (%)
https://static.tvmaze.com/uploads/images/original_untouched/435/1088342.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1088438.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1088374.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1088460.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1088461.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1088513.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1089133.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1089134.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1089135.jpg 1
 
0.8%
https://static.tvmaze.com/uploads/images/original_untouched/435/1089136.jpg 1
 
0.8%
Other values (110) 110
91.7%
2023-08-05T14:16:58.537560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 840
 
9.3%
t 720
 
8.0%
a 600
 
6.7%
s 480
 
5.3%
i 480
 
5.3%
o 480
 
5.3%
p 360
 
4.0%
c 360
 
4.0%
. 360
 
4.0%
g 360
 
4.0%
Other values (23) 3960
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 6360
70.7%
Other Punctuation 1320
 
14.7%
Decimal Number 1200
 
13.3%
Connector Punctuation 120
 
1.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 720
 
11.3%
a 600
 
9.4%
s 480
 
7.5%
i 480
 
7.5%
o 480
 
7.5%
p 360
 
5.7%
c 360
 
5.7%
g 360
 
5.7%
m 360
 
5.7%
e 360
 
5.7%
Other values (9) 1800
28.3%
Decimal Number
ValueCountFrequency (%)
8 195
16.2%
4 188
15.7%
1 188
15.7%
3 187
15.6%
0 170
14.2%
5 114
9.5%
9 62
 
5.2%
6 45
 
3.8%
7 26
 
2.2%
2 25
 
2.1%
Other Punctuation
ValueCountFrequency (%)
/ 840
63.6%
. 360
27.3%
: 120
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 120
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 6360
70.7%
Common 2640
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 720
 
11.3%
a 600
 
9.4%
s 480
 
7.5%
i 480
 
7.5%
o 480
 
7.5%
p 360
 
5.7%
c 360
 
5.7%
g 360
 
5.7%
m 360
 
5.7%
e 360
 
5.7%
Other values (9) 1800
28.3%
Common
ValueCountFrequency (%)
/ 840
31.8%
. 360
13.6%
8 195
 
7.4%
4 188
 
7.1%
1 188
 
7.1%
3 187
 
7.1%
0 170
 
6.4%
: 120
 
4.5%
_ 120
 
4.5%
5 114
 
4.3%
Other values (4) 158
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9000
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 840
 
9.3%
t 720
 
8.0%
a 600
 
6.7%
s 480
 
5.3%
i 480
 
5.3%
o 480
 
5.3%
p 360
 
4.0%
c 360
 
4.0%
. 360
 
4.0%
g 360
 
4.0%
Other values (23) 3960
44.0%

_links_self
Text

UNIQUE 

Distinct205
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:58.764140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length39
Min length39

Characters and Unicode

Total characters7995
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique205 ?
Unique (%)100.0%

Sample

1st rowhttps://api.tvmaze.com/episodes/2401587
2nd rowhttps://api.tvmaze.com/episodes/2449955
3rd rowhttps://api.tvmaze.com/episodes/2454680
4th rowhttps://api.tvmaze.com/episodes/2422258
5th rowhttps://api.tvmaze.com/episodes/2521740
ValueCountFrequency (%)
https://api.tvmaze.com/episodes/2401587 1
 
0.5%
https://api.tvmaze.com/episodes/2418533 1
 
0.5%
https://api.tvmaze.com/episodes/2454680 1
 
0.5%
https://api.tvmaze.com/episodes/2422258 1
 
0.5%
https://api.tvmaze.com/episodes/2521740 1
 
0.5%
https://api.tvmaze.com/episodes/2411106 1
 
0.5%
https://api.tvmaze.com/episodes/2463635 1
 
0.5%
https://api.tvmaze.com/episodes/2330730 1
 
0.5%
https://api.tvmaze.com/episodes/2513101 1
 
0.5%
https://api.tvmaze.com/episodes/2427755 1
 
0.5%
Other values (195) 195
95.1%
2023-08-05T14:16:59.120514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 820
 
10.3%
p 615
 
7.7%
s 615
 
7.7%
e 615
 
7.7%
t 615
 
7.7%
o 410
 
5.1%
a 410
 
5.1%
i 410
 
5.1%
. 410
 
5.1%
m 410
 
5.1%
Other values (16) 2665
33.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 5125
64.1%
Other Punctuation 1435
 
17.9%
Decimal Number 1435
 
17.9%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
p 615
12.0%
s 615
12.0%
e 615
12.0%
t 615
12.0%
o 410
8.0%
a 410
8.0%
i 410
8.0%
m 410
8.0%
h 205
 
4.0%
d 205
 
4.0%
Other values (3) 615
12.0%
Decimal Number
ValueCountFrequency (%)
4 293
20.4%
2 281
19.6%
9 131
9.1%
0 125
8.7%
5 125
8.7%
3 111
 
7.7%
7 102
 
7.1%
8 101
 
7.0%
1 94
 
6.6%
6 72
 
5.0%
Other Punctuation
ValueCountFrequency (%)
/ 820
57.1%
. 410
28.6%
: 205
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 5125
64.1%
Common 2870
35.9%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 820
28.6%
. 410
14.3%
4 293
 
10.2%
2 281
 
9.8%
: 205
 
7.1%
9 131
 
4.6%
0 125
 
4.4%
5 125
 
4.4%
3 111
 
3.9%
7 102
 
3.6%
Other values (3) 267
 
9.3%
Latin
ValueCountFrequency (%)
p 615
12.0%
s 615
12.0%
e 615
12.0%
t 615
12.0%
o 410
8.0%
a 410
8.0%
i 410
8.0%
m 410
8.0%
h 205
 
4.0%
d 205
 
4.0%
Other values (3) 615
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7995
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 820
 
10.3%
p 615
 
7.7%
s 615
 
7.7%
e 615
 
7.7%
t 615
 
7.7%
o 410
 
5.1%
a 410
 
5.1%
i 410
 
5.1%
. 410
 
5.1%
m 410
 
5.1%
Other values (16) 2665
33.3%
Distinct116
Distinct (%)56.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-08-05T14:16:59.355449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length34
Mean length33.97561
Min length33

Characters and Unicode

Total characters6965
Distinct characters26
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)41.5%

Sample

1st rowhttps://api.tvmaze.com/shows/7847
2nd rowhttps://api.tvmaze.com/shows/49630
3rd rowhttps://api.tvmaze.com/shows/58350
4th rowhttps://api.tvmaze.com/shows/59071
5th rowhttps://api.tvmaze.com/shows/67998
ValueCountFrequency (%)
https://api.tvmaze.com/shows/35820 10
 
4.9%
https://api.tvmaze.com/shows/63613 8
 
3.9%
https://api.tvmaze.com/shows/65800 8
 
3.9%
https://api.tvmaze.com/shows/60522 8
 
3.9%
https://api.tvmaze.com/shows/59275 7
 
3.4%
https://api.tvmaze.com/shows/66000 7
 
3.4%
https://api.tvmaze.com/shows/65796 6
 
2.9%
https://api.tvmaze.com/shows/65429 6
 
2.9%
https://api.tvmaze.com/shows/66417 6
 
2.9%
https://api.tvmaze.com/shows/65373 6
 
2.9%
Other values (106) 133
64.9%
2023-08-05T14:16:59.720814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 820
 
11.8%
s 615
 
8.8%
t 615
 
8.8%
h 410
 
5.9%
p 410
 
5.9%
a 410
 
5.9%
. 410
 
5.9%
o 410
 
5.9%
m 410
 
5.9%
6 210
 
3.0%
Other values (16) 2245
32.2%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 4510
64.8%
Other Punctuation 1435
 
20.6%
Decimal Number 1020
 
14.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 615
13.6%
t 615
13.6%
h 410
9.1%
p 410
9.1%
a 410
9.1%
o 410
9.1%
m 410
9.1%
e 205
 
4.5%
w 205
 
4.5%
c 205
 
4.5%
Other values (3) 615
13.6%
Decimal Number
ValueCountFrequency (%)
6 210
20.6%
5 164
16.1%
0 96
9.4%
3 92
9.0%
2 80
 
7.8%
9 78
 
7.6%
7 78
 
7.6%
8 76
 
7.5%
4 75
 
7.4%
1 71
 
7.0%
Other Punctuation
ValueCountFrequency (%)
/ 820
57.1%
. 410
28.6%
: 205
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 4510
64.8%
Common 2455
35.2%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 820
33.4%
. 410
16.7%
6 210
 
8.6%
: 205
 
8.4%
5 164
 
6.7%
0 96
 
3.9%
3 92
 
3.7%
2 80
 
3.3%
9 78
 
3.2%
7 78
 
3.2%
Other values (3) 222
 
9.0%
Latin
ValueCountFrequency (%)
s 615
13.6%
t 615
13.6%
h 410
9.1%
p 410
9.1%
a 410
9.1%
o 410
9.1%
m 410
9.1%
e 205
 
4.5%
w 205
 
4.5%
c 205
 
4.5%
Other values (3) 615
13.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6965
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 820
 
11.8%
s 615
 
8.8%
t 615
 
8.8%
h 410
 
5.9%
p 410
 
5.9%
a 410
 
5.9%
. 410
 
5.9%
o 410
 
5.9%
m 410
 
5.9%
6 210
 
3.0%
Other values (16) 2245
32.2%

summary
Text

MISSING 

Distinct110
Distinct (%)100.0%
Missing95
Missing (%)46.3%
Memory size1.7 KiB
2023-08-05T14:17:00.052832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length653
Median length219
Mean length184.42727
Min length63

Characters and Unicode

Total characters20287
Distinct characters80
Distinct categories10 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique110 ?
Unique (%)100.0%

Sample

1st row<p>A look at the Port Building, a stunning, gravity-defying office complex building that appears to hover over a 19th-century fire station, and uses a borehole energy system that makes it one of the most energy efficient buildings in Europe.</p>
2nd row<p>Seon Heo gathers his resolve to tell Mu Yeong how he really feels, but Mu Yeong doesn't accept his confession of love.</p>
3rd row<p>Markus believes Christmas leftovers are best enjoyed in the dead of night - something the participants get to experience, good and bad. Most painful actually.</p>
4th row<p>Excited to finally have the son he always wanted, Chief Crawford focuses on being a new father while the townspeople build a utopia — inside Dobby.</p>
5th row<p>Karen replaces everyone in town with actors to impress Elon Musk, Kevin gets an inflatable girlfriend and Dusty obsesses about impressing his father.</p>
ValueCountFrequency (%)
the 148
 
4.5%
to 110
 
3.4%
a 108
 
3.3%
and 104
 
3.2%
of 61
 
1.9%
in 46
 
1.4%
their 40
 
1.2%
with 38
 
1.2%
his 36
 
1.1%
her 34
 
1.0%
Other values (1536) 2547
77.8%
2023-08-05T14:17:00.569720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3153
15.5%
e 1876
 
9.2%
t 1350
 
6.7%
a 1281
 
6.3%
s 1159
 
5.7%
o 1144
 
5.6%
i 1138
 
5.6%
n 1041
 
5.1%
r 985
 
4.9%
h 847
 
4.2%
Other values (70) 6313
31.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15467
76.2%
Space Separator 3164
 
15.6%
Uppercase Letter 587
 
2.9%
Other Punctuation 538
 
2.7%
Math Symbol 458
 
2.3%
Dash Punctuation 60
 
0.3%
Decimal Number 10
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Initial Punctuation 1
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1876
12.1%
t 1350
 
8.7%
a 1281
 
8.3%
s 1159
 
7.5%
o 1144
 
7.4%
i 1138
 
7.4%
n 1041
 
6.7%
r 985
 
6.4%
h 847
 
5.5%
l 589
 
3.8%
Other values (18) 4057
26.2%
Uppercase Letter
ValueCountFrequency (%)
C 61
 
10.4%
A 58
 
9.9%
T 56
 
9.5%
M 40
 
6.8%
S 34
 
5.8%
L 33
 
5.6%
D 29
 
4.9%
I 26
 
4.4%
F 26
 
4.4%
B 25
 
4.3%
Other values (16) 199
33.9%
Other Punctuation
ValueCountFrequency (%)
. 184
34.2%
, 138
25.7%
/ 115
21.4%
' 55
 
10.2%
! 14
 
2.6%
; 13
 
2.4%
" 8
 
1.5%
: 5
 
0.9%
& 3
 
0.6%
? 3
 
0.6%
Decimal Number
ValueCountFrequency (%)
1 5
50.0%
0 2
 
20.0%
3 1
 
10.0%
9 1
 
10.0%
8 1
 
10.0%
Space Separator
ValueCountFrequency (%)
3153
99.7%
  6
 
0.2%
5
 
0.2%
Dash Punctuation
ValueCountFrequency (%)
- 50
83.3%
8
 
13.3%
2
 
3.3%
Math Symbol
ValueCountFrequency (%)
> 229
50.0%
< 229
50.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 16054
79.1%
Common 4233
 
20.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1876
11.7%
t 1350
 
8.4%
a 1281
 
8.0%
s 1159
 
7.2%
o 1144
 
7.1%
i 1138
 
7.1%
n 1041
 
6.5%
r 985
 
6.1%
h 847
 
5.3%
l 589
 
3.7%
Other values (44) 4644
28.9%
Common
ValueCountFrequency (%)
3153
74.5%
> 229
 
5.4%
< 229
 
5.4%
. 184
 
4.3%
, 138
 
3.3%
/ 115
 
2.7%
' 55
 
1.3%
- 50
 
1.2%
! 14
 
0.3%
; 13
 
0.3%
Other values (16) 53
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20262
99.9%
Punctuation 16
 
0.1%
None 9
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3153
15.6%
e 1876
 
9.3%
t 1350
 
6.7%
a 1281
 
6.3%
s 1159
 
5.7%
o 1144
 
5.6%
i 1138
 
5.6%
n 1041
 
5.1%
r 985
 
4.9%
h 847
 
4.2%
Other values (63) 6288
31.0%
Punctuation
ValueCountFrequency (%)
8
50.0%
5
31.2%
2
 
12.5%
1
 
6.2%
None
ValueCountFrequency (%)
  6
66.7%
ë 2
 
22.2%
í 1
 
11.1%

Interactions

2023-08-05T14:16:51.972155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:49.857496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.396197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.925174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.471028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:52.080918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:49.966049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.509489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.034677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.576482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:52.184006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.073306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.608223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.149008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.675538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:52.293169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.185901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.719505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.264440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.782510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:52.388561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.285406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:50.814978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.364619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-05T14:16:51.873355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-05T14:17:00.689571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
idid_embeddedseasonnumberruntimetype
id1.0000.4580.058-0.029-0.1570.352
id_embedded0.4581.000-0.679-0.251-0.0330.000
season0.058-0.6791.0000.203-0.0580.000
number-0.029-0.2510.2031.000-0.1451.000
runtime-0.157-0.033-0.058-0.1451.0000.000
type0.3520.0000.0001.0000.0001.000

Missing values

2023-08-05T14:16:52.549449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-05T14:16:52.803942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-05T14:16:52.966250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

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